Carbon dioxide/methanol conversion cycle based on cascade enzymatic reactions supported on superparamagnetic nanoparticles
The conversion of carbon dioxide into important industrial feedstock is a subject of growing interest in modern society. A possible way to achieve this goal is by carrying out the CO2/methanol cascade reaction, allowing the recycle of CO2 using either chemical catalysts or enzymes. Efficient and sel...
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Published in | Anais da Academia Brasileira de Ciências Vol. 90; no. 1 suppl 1; pp. 593 - 606 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Brazil
Academia Brasileira de Ciências
01.01.2018
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Subjects | |
Online Access | Get full text |
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Summary: | The conversion of carbon dioxide into important industrial feedstock is a subject of growing interest in modern society. A possible way to achieve this goal is by carrying out the CO2/methanol cascade reaction, allowing the recycle of CO2 using either chemical catalysts or enzymes. Efficient and selective reactions can be performed by enzymes; however, due to their low stability, immobilization protocols are required to improve their performance. The cascade reaction to reduce carbon dioxide into methanol has been explored by the authors, using, sequentially, alcohol dehydrogenase (ADH), formaldehyde dehydrogenase (FalDH), and formate dehydrogenase (FDH), powered by NAD+/NADH and glutamate dehydrogenase (GDH) as the co-enzyme regenerating system. All the enzymes have been immobilized on functionalized magnetite nanoparticles, and their reactions investigated separately in order to establish the best performance conditions. Although the stepwise scheme led to only 2.3% yield of methanol per NADH; in a batch system under CO2 pressure, the combination of the four immobilized enzymes increased the methanol yield by 64 fold. The studies indicated a successful regeneration of NADH in situ, envisaging a real possibility of using immobilized enzymes to perform the cascade CO2-methanol reaction. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0001-3765 1678-2690 1678-2690 |
DOI: | 10.1590/0001-3765201720170330 |